1. Automation of ITSM & Lifecycle Workflows. Automate asset and lifecycle management processes including end-of-life tracking, refresh orchestration, and compliance monitoring. Rapidly develop AI skills and automation patterns necessary to model optimal paths for EOL work orchestration and completion. This includes building a catalog of repeatable skills for activities such as code-based updates (.net / java version updates) and server and O/ S migrations. Design and implement automation across incident, problem, and change management workflows. Build end-to-end workflows that connect trigger, decision, and execution points to reduce manual effort and cycle time .. AI Enablement. Integrate AI into operational workflows to improve incident triage, routing, root cause summarization, change risk prediction, and lifecycle risk modeling. Leverage enterprise AI capabilities such as Claude Code, Azure AI/ OpenAI, Gemini and internal AI platforms where applicable .. Platform & Data Integration. Integrate and optimize ServiceNow, Lean. IX, Apptio, CMDB, and related enterprise platforms. Ensure clean, automated data flow across systems to improve interoperability, reduce duplicate entry, and strengthen decision support .. Observability & Insights. Build dashboards and insights for lifecycle risk, incident patterns, root cause trends, and change success/failure rates. Enable real-time operational visibility to support better prioritization and faster action. Expected Outcomes. Establish a scalable catalog of reusable AI-driven automation capabilities that application and technology teams leverage annually to plan, orchestrate, and execute EOL lifecycle activities with minimal manual effort. Accelerate EOL lifecycle execution through AI-enabled orchestration, simplifying complex, cross-team workflows and reducing time to remediate unsupported technologies across the enterprise. Develop standardized automation patterns and playbooks that group and sequence logical EOL activities (e.g., discovery, impact analysis, remediation planning, execution), eliminating redundant effort and driving consistency. Enable intelligent, application and technology-centric EOL planning, delivering clear visualization, inventory, and AI-recommended action plans for each application or technology to streamline decision-making and execution. Drive rapid adoption of AI skills and tooling across ITSM and TLM teams, embedding AI into day-to-day operations to continuously optimize lifecycle management, reduce waste, and improve throughput. Improve enterprise visibility into EOL risk and progress through automated insights, enabling proactive planning, faster execution, and measurable reduction in lifecycle-related risk exposure. Use your skills to make an impact Required Qualifications. Bachelor's degree or equivalent work experience .0 or more years of engineering experience with automation, integration, or software/platform engineering. Experience with AI tools including Azure, Claude Code, OpenAI - Demonstrated experience simplifying complex operational processes through software engineering and workflow automation. Strong experience with APIs, scripting, and software development using technologies such as Python and/or JavaScript. Experience with ServiceNow development, configuration, integration, or workflow automation. Experience with cloud technologies and modern automation frameworks, with Azure preferred. Strong communication, systems thinking, and the ability to collaborate effectively across organizational boundaries. Passion for automation, simplification, and improving how work gets done across enterprise IT operations. Preferred Qualifications. Experience applying AI to IT operations, workflow orchestration, or enterprise service management. Knowledge of ITSM disciplines including incident, problem, and change management. Knowledge of CMDB, asset management, and technology lifecycle management practices. Experience integrating enterprise platforms such as ServiceNow, Lean. IX, Apptio, and related data/reporting tools. Strong data mindset with experience using metrics to improve quality, automation outcomes, and operational performance.